E-Learning has proven to be the only resort as a replacement of traditional face-to-face learning methods in the current global lockdown due to COVID-19 pandemic. Academic institutions across the globe have invested heavily into E-Learning and the majority of the courses offered in traditional classroom mode have been converted into E-Learning mode. The success of E-Learning initiatives needs to be ensured to make it a sustainable mode of learning. The objective of the current study is to propose a holistic E-Learning service framework to ensure effective delivery and use of E-Learning Services that contributes to sustainable learning and academic performance. Based on an extensive literature review, a proposed theoretical model has been developed and tested empirically. The model identifies a broad range of success determinants and relates them to different success measures, including learning and academic performance. The proposed model was validated with the response from 397 respondents involved with an E-Learning system in the top five public universities in the southern region of Saudi Arabia through the Partial Least Squares regression technique using SmartPLS software. Five main factors (Learner’s Quality, Instructor’s Quality, Information’s Quality, System’s Quality and Institutional Quality) were identified as a determinant of E-Learning service performance which together explains 48.7% of the variance of perceived usefulness of ELS, 71.2% of the variance of use of the E-Learning system. Perceived usefulness of ELS and use of ELS together explain 70.6% of learning and academic performance of students. Hence the framework will help achieve the sustainable and successful adoption of E-Learning services.

For mobile clients, sufficient resources with the assurance of efficient performance and energy efficiency are the core concerns. This article mainly considers this need and proposes a resourceful architecture, called mRARSA that addresses the critical need in a mobile cloud environment. This architecture consists of cloud resources, mobile devices, and a set of functional components. The performance efficiency evaluates implementing the proposed context-aware multi-criteria decision offloading algorithm. This algorithm considers both device context (network parameters) and application content (task size) at run time when offloading an executable code to allocate the cloud resources. The appropriate resources select based on offloading decisions and via the wireless communication channels. The architecture's remarkable component is the signal strength analyzer that determines the signal quality (e.g.-60 dBm) and contributes to performance efficiency. The proposed prototype model has implemented several times to monitor the performance efficiency, mobility, performance at communication barriers, and the outcomes of resource-demanding application's execution. Results indicate performance improvement, such as the algorithm appropriately decides the cloud resources based on device network context, application content, mobility, and the signal strength quality and range. Moreover, the results also show significant improvement in achieving performance and energy efficiency. Sufficient resources and performance efficiency are the most significant features that distinguish this framework from the other existing frameworks.

Estimation of fault classification and location in a multi-terminal high voltage direct current (MT–HVdc) transmission system is a challenging problem and is considered to be a fundamental maneuver of dc grid protection. This research paper critically reviews traveling and non-travelling wave methods of classification and location of dc faults in multi-terminal HVdc transmission systems. Detailed mathematical analysis of MT–HVdc systems composed of high grounding resistance, cable and overhead line segments, and bipolar coupled transmission network under healthy and faulty conditions, are evaluated. The gravity of this research paper addresses benefits and shortcomings of traveling and non-traveling wave methods and futuristic techniques of fault classification and location.

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This study addresses the real-time issue of managing an academic program's documents in a university environment. In practice, document classification from a corpus is challenging when the dataset size is large, and the complexity increases if to meet some specific document management requirements. This study presents a practical approach to grouping documents based on a content similarity measure. The approach analyzes the state-of-the-art clustering algorithms performance, considers Hamiltonian graph properties and a distance function. The distance function measures (1) the content similarity between the documents and (2) the distances between the produced clusters. The proposed algorithm improves clusters’ quality by applying Hamiltonian graph properties. One of the significant characteristics of the proposed function is that it determines document types from the corpus. Hence, this does not require the initial assumption of cluster number before the algorithm execution. This approach omits the arbitrary primordial option of k-centroids of the k-means algorithm, reduces computational complexities, and overcomes some limitations of commonly practicing clustering algorithms. The proposed approach enables an effective way of document organization opportunities to the information systems developers when designing document management systems.